• Amit Tidke
• Sarika Kamble
• Padmasinha Chitte
• Ishita Tripute

Owing to changing climatic conditions, crops often get affected, as a result of which agricultural yield decreases drastically. If the condition gets worse, crops may get vulnerable towards infections caused by fungal, bacterial, virus, etc. diseases causing agents. The method that can be adopted to prevent plant loss can be carried out by real-time prediction of plant diseases. Our proposed model provides an automatic method to predict leaf disease in a plant using a trained dataset of potato leaf images. The test set is used to check whether an image entered into the system contains disease or not. If not, it is considered to be healthy, otherwise the disease if that leaf is predicted and the prevention of plant disease is proposed automatically. Further, the rodent causing disease is also identified with image analysis performed on the image certified by biologists and scientists. This model provides an accuracy of the results generated using different cluster sizes, optimized experimentally, with image classification using CNN. Our model provides useful estimation and prediction of disease causing agent with necessary precautions.

Crop, leaf disease, real-time prediction, potato leaf, using CN.

"Leaf Disease Prediction Using CNN", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 5, page no.214-218, May-2023, Available :http://www.jetir.org/papers/JETIRFX06036.pdf

"Leaf Disease Prediction Using CNN", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 5, page no. pp214-218, May-2023, Available at : http://www.jetir.org/papers/JETIRFX06036.pdf